
#import the important packages
import pandas as pd #library used for data manipulation and analysis
import numpy as np # library used for working with arrays.
import matplotlib.pyplot as plt # library for plots and visualisations
import seaborn as sns # library for visualisations
import plotly.express as px # library for visualisations
%matplotlib inline 

import scipy.stats as stats # this library contains a large number of probability distributions as well as a growing library of statistical functions.
from scipy.stats import *

import warnings # ignore warnings
warnings.filterwarnings("ignore")


# install pandas sql
!pip install -U pandasql 
import pandasql as psql # python library that allows manipulation of a Pandas Dataframe using SQL
from pandasql import sqldf

Requirement already satisfied: pandasql in c:\users\abhinav roy\anaconda3\lib\site-packages (0.7.3)
Requirement already satisfied: sqlalchemy in c:\users\abhinav roy\anaconda3\lib\site-packages (from pandasql) (1.4.32)
Requirement already satisfied: pandas in c:\users\abhinav roy\anaconda3\lib\site-packages (from pandasql) (1.4.2)
Requirement already satisfied: numpy in c:\users\abhinav roy\anaconda3\lib\site-packages (from pandasql) (1.21.5)
Requirement already satisfied: python-dateutil>=2.8.1 in c:\users\abhinav roy\anaconda3\lib\site-packages (from pandas->pandasql) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\abhinav roy\anaconda3\lib\site-packages (from pandas->pandasql) (2021.3)
Requirement already satisfied: six>=1.5 in c:\users\abhinav roy\anaconda3\lib\site-packages (from python-dateutil>=2.8.1->pandas->pandasql) (1.16.0)
Requirement already satisfied: greenlet!=0.4.17 in c:\users\abhinav roy\anaconda3\lib\site-packages (from sqlalchemy->pandasql) (1.1.1)


#1A
P_A = (400/2000) # P(Planned to purchase| Actually placed an order)
print('Joint probability of the people who planned to purchase and actually placed an order is %1.4f' % P_A)

Joint probability of the people who planned to purchase and actually placed an order is 0.2000


#2B
P_B = (400 / 2000 )/ (500 / 2000) # P(Actually placed an order | Planned to purchase and actually placed an order)
print('Joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase is %1.4f' % P_B)

Joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase is 0.8000


p2 = 0.05 # failure rate
n2 = 10 # sample size
k2 = np.arange(11) # array of 10 items


binom = binom.pmf(k2, n2, p2)
binom

array([5.98736939e-01, 3.15124705e-01, 7.46347985e-02, 1.04750594e-02,
       9.64808106e-04, 6.09352488e-05, 2.67259863e-06, 8.03789062e-08,
       1.58642578e-09, 1.85546875e-11, 9.76562500e-14])


#2A
print(' Probability that none of the items are defective is %1.4f' % binom[0])

 Probability that none of the items are defective is 0.5987


#2B
print(' Probability that exactly one of the items is defective is %1.4f' % binom[1])

 Probability that exactly one of the items is defective is 0.3151


import scipy.stats as stats # this library contains a large number of probability distributions as well as a growing library of statistical functions.
from scipy.stats import *


cubinom = binom.cdf(k2, n2, p2)
cubinom

array([0.59873694, 0.91386164, 0.98849644, 0.9989715 , 0.99993631,
       0.99999725, 0.99999992, 1.        , 1.        , 1.        ,
       1.        ])


#2C
print(' Probability that two or fewer of the items are defective is %1.4f' % cubinom[2])

 Probability that two or fewer of the items are defective is 0.9885


#2D
print(' Probability that three or more of the items are defective  is %1.4f' % (1-cubinom[2]))

 Probability that three or more of the items are defective  is 0.0115


rate =  3 # number of cars per week
n3    =  np.arange(0,31)


cumpoisson = poisson.cdf(n3,rate)
cumpoisson

array([0.04978707, 0.19914827, 0.42319008, 0.64723189, 0.81526324,
       0.91608206, 0.96649146, 0.9880955 , 0.99619701, 0.99889751,
       0.99970766, 0.99992861, 0.99998385, 0.9999966 , 0.99999933,
       0.99999988, 0.99999998, 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        ])


#3A
print('Probability that in a given week he will sell some cars is %1.4f' % (1-cumpoisson[0]))

Probability that in a given week he will sell some cars is 0.9502


#3B
print('Probability that in a given week he will sell some cars is %1.4f' % (cumpoisson[4]-cumpoisson[2]))

Probability that in a given week he will sell some cars is 0.3921


#3C
plt.plot(n3, cumpoisson, 'o-')
plt.title('Poisson Distribution Function')
plt.xlabel('Number of cars sold per week')
plt.ylabel('Cumulative probability of cars sold per-week')
plt.show()


p4 = 0.868
n4 = 3
k4 = np.arange(4)


binom4 = binom.pmf(k4, n4, p4)
binom4

array([0.00229997, 0.0453721 , 0.2983559 , 0.65397203])


#4A
print('Probability that all three orders will be recognised correctly is %1.4f' % binom4[3])

Probability that all three orders will be recognised correctly is 0.6540


#4B
print('Probability that none of the three orders will be recognised correctly is %1.4f' % binom4[0])

Probability that none of the three orders will be recognised correctly is 0.0023


cumbinom4 = binom.cdf(k4, n4, p4)
cumbinom4

array([0.00229997, 0.04767206, 0.34602797, 1.        ])


#4C
cumbinom4c = 1 - (cumbinom4[0] + cumbinom4[1])
print('Probability that at least two of the three orders will be recognised correctly is %1.4f' % cumbinom4c)

Probability that at least two of the three orders will be recognised correctly is 0.9500


#load the dataset
df = pd.read_csv("Basketball.csv")


df.head()


# dataframe shape
df.shape

(61, 13)


# dataframe information
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   Team                 61 non-null     object
 1   Tournament           61 non-null     int64 
 2   Score                61 non-null     object
 3   PlayedGames          61 non-null     object
 4   WonGames             61 non-null     object
 5   DrawnGames           61 non-null     object
 6   LostGames            61 non-null     object
 7   BasketScored         61 non-null     object
 8   BasketGiven          61 non-null     object
 9   TournamentChampion   61 non-null     object
 10  Runner-up            61 non-null     object
 11  TeamLaunch           61 non-null     object
 12  HighestPositionHeld  61 non-null     int64 
dtypes: int64(2), object(11)
memory usage: 6.3+ KB


# dataframe description
df.describe(include = 'all').T


#Check the null values in all fields
df.isna().sum()

Team                   0
Tournament             0
Score                  0
PlayedGames            0
WonGames               0
DrawnGames             0
LostGames              0
BasketScored           0
BasketGiven            0
TournamentChampion     0
Runner-up              0
TeamLaunch             0
HighestPositionHeld    0
dtype: int64


#Replace NAN values with 0 since dataset is not large
df.replace(np.nan, 0, inplace = True)


df.tail()


#Substring to pick the first year as Team Launch year only
df['TeamLaunch'] = df['TeamLaunch'].apply(lambda x: x[0:4])


df.tail()


# Convert all columns except Team to float datatype
cols = df.columns
df[cols[1:]] = df[cols[1:]].apply(pd.to_numeric, errors = 'coerce', axis = 1)


# dataframe information
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Team                 61 non-null     object 
 1   Tournament           61 non-null     float64
 2   Score                60 non-null     float64
 3   PlayedGames          60 non-null     float64
 4   WonGames             60 non-null     float64
 5   DrawnGames           60 non-null     float64
 6   LostGames            60 non-null     float64
 7   BasketScored         60 non-null     float64
 8   BasketGiven          60 non-null     float64
 9   TournamentChampion   9 non-null      float64
 10  Runner-up            13 non-null     float64
 11  TeamLaunch           61 non-null     float64
 12  HighestPositionHeld  61 non-null     float64
dtypes: float64(12), object(1)
memory usage: 6.3+ KB


# dataframe description
df.describe(include = 'all').T


#Plotting boxplot for Individual fields for dataframe df
plt.figure(figsize=(15,7))
index = 1
for col in df.columns[1:]:
    plt.subplot(4,3,index)
    sns.boxplot(df[col], color = 'violet')
    index += 1;
plt.tight_layout()


#plotting heatmap for dataframe df
plt.figure(figsize = (15,7))
sns.heatmap(df.corr(), annot = True, cmap = 'rainbow');


# create new dataframe df2 to add derived fields
df2 = df.copy(deep = True)
df2.head()


#Add new field to combine the number of times a Team finished in the Top 2
top_2_finishes = df2['TournamentChampion'] + df2['Runner-up']
df2.insert(11, 'TopTwoFinishes', top_2_finishes)
df2.head()


#Add new field to find the Basket difference for a Team
basket_diff = df2['BasketScored'] - df2['BasketGiven']
df2.insert(9, 'BasketDiff', basket_diff)
df2.head()


#Add new field to find the Net Game Points for individual Team as a better way to analyse a Team's performance
# WonGames = 3 points, LostGames = 0 points, DrawnGames = 1 point
net_game_points = (df2['WonGames'] * 3) + (df2['LostGames'] * 0) + (df2['DrawnGames'] * 1)
df2.insert(7, 'NetGamePoints', net_game_points)
df2.head()


# Plotting the heat map for dataframe df2
plt.figure(figsize = (15,7))
sns.heatmap(df2.corr(), annot = True, cmap = 'rainbow');


# Plotting the pairplot for dataframe df2
sns.pairplot(data = df2[['Tournament','Score','NetGamePoints','BasketDiff','TopTwoFinishes']], corner=True);


# Plotting the line plot for dataframe df2
plt.figure(figsize=(20,10))
sns.lineplot('Team', 'Tournament', data = df2, color='Violet')
sns.lineplot('Team', 'PlayedGames', data = df2, color='Black')
sns.lineplot('Team', 'Score', data = df2, color='Orange')
sns.lineplot('Team', 'NetGamePoints', data = df2, color='Green')
sns.lineplot('Team', 'BasketDiff', data = df2, color='Red')
sns.lineplot('Team', 'TopTwoFinishes', data = df2, color='Blue')
plt.grid()
plt.xticks(rotation=45)
plt.ylabel('Count')
plt.legend(labels=['Tournament','Played Games', 'Score', 'Net Game Points', 'Basket Difference', 'Top Two Finishes'])
plt.title('Teams vs Performance metrics in Tournaments');


# create new dataframe df3 to analyse the top Teams
df3 = df2[df2['TopTwoFinishes'] > 0].copy(deep = True)
df3


# Plotting the pie plot for dataframe df3
plt.figure(figsize=(15,7))
plt.pie(df3['TopTwoFinishes'], labels= df3['Team'])
plt.title('Teams with most the most Top 2 finishes in Tournaments')
plt.show()


#Plotting lineplot for Individual fields for dataframe df3
plt.figure(figsize=(20,10))
sns.lineplot(y = 'TopTwoFinishes', x = 'Tournament', data = df3, color='Purple')
sns.lineplot(y = 'TopTwoFinishes', x = 'PlayedGames', data = df3, color='Black')
sns.lineplot(y = 'TopTwoFinishes', x = 'Score', data = df3, color='Orange')
sns.lineplot(y = 'TopTwoFinishes', x = 'NetGamePoints', data = df3, color='Green')
sns.lineplot(y = 'TopTwoFinishes', x = 'BasketDiff', data = df3, color='Red')
plt.grid()
plt.xlabel('x-axis')
plt.legend(labels=['Tournament','Played Games', 'Score', 'Net Game Points', 'Basket Difference'])
plt.title('Top Two Finishes vs Performance metrics in Tournaments');


#Plotting lineplot subplot for Individual fields for dataframe df3
plt.figure(figsize=(15,7))
index = 1
for col in df.columns[1:]:
    plt.subplot(4,3,index)
    sns.lineplot(y = 'TopTwoFinishes', x = col, data = df3)
    index += 1
    plt.grid();
plt.tight_layout()


#Plotting histplot subplot for Individual fields for dataframe df3
plt.figure(figsize=(20,10))
index = 1
for col in df.columns[1:]:
    plt.subplot(4,3,index)
    sns.histplot(x = col, data = df3, kde = True)
    index += 1
    plt.grid();
plt.tight_layout()


#load the dataset df_1
df_1 = pd.read_csv("CompanyX_EU.csv")


df_1.head()


# dataframe df_1 information
df_1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 662 entries, 0 to 661
Data columns (total 6 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Startup         662 non-null    object
 1   Product         656 non-null    object
 2   Funding         448 non-null    object
 3   Event           662 non-null    object
 4   Result          662 non-null    object
 5   OperatingState  662 non-null    object
dtypes: object(6)
memory usage: 31.2+ KB


# dataframe df_1 description
df_1.describe(include = 'all').T


# Check the datatypes of each attribute in dataframe df_1
df_1.dtypes

Startup           object
Product           object
Funding           object
Event             object
Result            object
OperatingState    object
dtype: object


# Check for null values in the attributes in dataframe df_1
df_1.isna().sum()

Startup             0
Product             6
Funding           214
Event               0
Result              0
OperatingState      0
dtype: int64


# Drop the null values in dataframe df_1
df_1.dropna(inplace = True)


# Check the null values in all fields in dataframe df_1
df_1.isna().sum()

Startup           0
Product           0
Funding           0
Event             0
Result            0
OperatingState    0
dtype: int64


df_1.head()


# create a new dataframe df_2 for further analysis
df_2 = df_1.copy(deep = True)
df_2.head()


# Convert the Funding features to a numerical value
df_2.loc[:,'Funds_in_million'] = df_2['Funding'].apply(lambda x: float(x[1:-1])/1000 if x[-1] == 'K' else (float(x[1:-1])*1000 if x[-1] == 'B' else float(x[1:-1])))


# dataframe df_2 information
df_2.head()


# Plot box plot for Funding for dataframe df_2
fig = px.box(df_2, x = "Funding")
fig.show()


# Plot box plot for funds in million for dataframe df_2
#plt.boxplot(df_2.Funds_in_million)
fig = px.box(df_2, x="Funds_in_million")
fig.show()


# Plot box plot for funds in million for dataframe df_2 using matplotlib
plot = plt.boxplot(df_2.Funds_in_million)
plt.show();


# calculate the lower fence value for the boxplot
lower_fence = plot['caps'][0].get_data()[1][1]
lower_fence

0.005


# calculate the upper fence value for the boxplot
upper_fence = plot['caps'][1].get_data()[1][1]
upper_fence

22.0


# Startups that have received more funding than boxplot plot outlier
df_2[df_2['Funds_in_million'] > upper_fence].head()


# Check the number of outliers greater than the upper fence
up_fence_outliers = df_2['Startup'][df_2['Funds_in_million'] > upper_fence].count()

print("Number of outliers greater than the upper fence is", up_fence_outliers)

Number of outliers greater than the upper fence is 60


# Check frequency of the OperatingState features classes
df_2.OperatingState.value_counts()

Operating    319
Acquired      66
Closed        57
Ipo            4
Name: OperatingState, dtype: int64


# Plot frequency of the OperatingState features classes
cplot = px.bar(df_2, x = "OperatingState" , color = 'OperatingState')
cplot.show()


# Count of startups that are Operating
df_2['Startup'][df_2['OperatingState'] == 'Operating'].count()

319


# Count of startups that have Closed
df_2['Startup'][df_2['OperatingState'] == 'Closed'].count()

57


# Plot a sublplot to compare the Funds raised by companies that are still operating vs companies that closed down
fig, ax = plt.subplots(1,2)
fig.set_figwidth(15)
fig.set_figheight(7)

sns.distplot(x = df_2['Funds_in_million'][df_2['OperatingState'] == 'Operating'], ax = ax[0])
ax[0].set_title("Funding for Operating Startups")


sns.distplot(x = df_2['Funds_in_million'][df_2['OperatingState'] == 'Closed'], ax = ax[1])
ax[1].set_title("Funding for Closed Startups")

plt.show();


# import z test to find z_stat, p_val
from statsmodels.stats.weightstats import ztest
alpha = 0.05 # considering 95% Confidence Interval

operating = df_2['Funds_in_million'][df_2['OperatingState'] == 'Operating']
closed = df_2['Funds_in_million'][df_2['OperatingState'] == 'Closed']

z_stat, p_val = ztest(operating, closed)
z_stat, p_val

(1.1382924515740138, 0.25499838490589677)


# check if calculated p_val is greater/ less than alpha
if p_val > alpha:
    print("Since p_val of {} is greater than alpha value of {}, we Fail to reject H0".format(round(p_val,2), alpha))
else:
    print("Since p_val of {} is less than alpha value of {}, we Reject H0".format(p_val, alpha))

Since p_val of 0.25 is greater than alpha value of 0.05, we Fail to reject H0


# find the z critical at alpha = 0.05
z_crit = norm.isf(0.05/2)
z_crit

1.9599639845400545


# check if calculated z_stat is greater/ less than z_crit
if z_crit > z_stat:
    print("Since z_crit of {} is greater than z_stat of {}, we Fail to reject H0".format(round(z_crit,2), round(z_stat,2)))
else:
    print("Since z_crit of {} is less than z_stat of {}, we Reject H0".format(round(z_crit,2), round(z_stat,2)))

Since z_crit of 1.96 is greater than z_stat of 1.14, we Fail to reject H0


# create a new dataframe df_3
df_3 = df_2.copy(deep = True)
df_3.head()


# Check frequency distribution of Result variables
df_3['Result'].value_counts()

Contestant         312
Finalist            65
Audience choice     25
Winner              25
Runner up           19
Name: Result, dtype: int64


# Plot frequency of the Result features classes
cplot1 = px.bar(df_3, x = "Result" , color = 'Result')
cplot1.show()


# Sort dataframe df_3 base on the Result of each Startup
df_3['Result'].value_counts()

Contestant         312
Finalist            65
Audience choice     25
Winner              25
Runner up           19
Name: Result, dtype: int64


# Check the total contestants within the Startups
total_contestants = df_3['Result'].value_counts()[0]
total_contestants

312


df_4 = df_3[df_3['OperatingState'] == 'Operating'].groupby('Result').count()


df_4


# Calculate the number of Winners and Contestants Startups still operating
winners_operating = df_4.loc['Winner','Startup']
contestants_operating = df_4.loc['Contestant','Startup']
winners_operating, contestants_operating

(18, 214)


# Calculate the percentage of Winners and Contestants Startups still operating
per_win_op = round((winners_operating/total_contestants) * 100, 2)
per_con_op = round((contestants_operating/total_contestants) * 100, 2)

print("The percentage of winners that are still operating is {}%  and percentage of contestants that are still operating is {}%".format(per_win_op, per_con_op))

The percentage of winners that are still operating is 5.77%  and percentage of contestants that are still operating is 68.59%


# Using SQL to manipulate dataframe df_3
# Calculate the number of Winners and Contestants Startups still operating
win_operating = psql.sqldf("select Funds_in_million from df_3 where OperatingState = 'Operating' and Result = 'Winner'")
con_operating = psql.sqldf("select Funds_in_million from df_3 where OperatingState = 'Operating' and Result = 'Contestant'")


# import z test to find z_stat, p_val
from statsmodels.stats.weightstats import ztest
alpha = 0.05 # considering 95% Confidence Interval

z_stat1, p_val1 = ztest(win_operating, con_operating)
z_stat1, p_val1

(array([0.08151046]), array([0.93503601]))


# check if calculated p_val is greater/ less than alpha
if p_val1 > alpha:
    print("Since p_val of {} is greater than alpha value of {}, we Fail to reject H0".format(round(0.93503601,2), alpha))
else:
    print("Since p_val of {} is less than alpha value of {}, we Reject H0".format(round(0.93503601,2), alpha))

Since p_val of 0.94 is greater than alpha value of 0.05, we Fail to reject H0


# find the z critical at alpha = 0.05
z_crit = norm.isf(0.05/2)
z_crit

1.9599639845400545


# check if calculated z_stat is greater/ less than z_crit
if z_crit > z_stat:
    print("Since z_crit of {} is greater than z_stat of {}, we Fail to reject H0".format(round(z_crit,2), round(0.08151046,2)))
else:
    print("Since z_crit of {} is less than z_stat of {}, we Reject H0".format(round(z_crit,2), round(0.08151046,2)))

Since z_crit of 1.96 is greater than z_stat of 0.08, we Fail to reject H0


# Add a new field to dataframe df_3 to check the Year values present in Event names
df_3['Event_Year'] = df_3.Event.apply(lambda x : x[-4:])


# Select only the Event that has ‘disrupt’ keyword from 2013 onwards.
df_5 = psql.sqldf("select * from df_3 where lower(Event) like 'disrupt%' and Event_Year >= 2013")
df_5.head()


# Check the rowsxcolumns in dataframe df_5
df_5.shape

(193, 8)

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
0	Team 1	86	4385	2762	1647	552	563	5947	3140	33	23	1929	1
1	Team 2	86	4262	2762	1581	573	608	5900	3114	25	25	1929	1
2	Team 3	80	3442	2614	1241	598	775	4534	3309	10	8	1929	1
3	Team 4	82	3386	2664	1187	616	861	4398	3469	6	6	1931to32	1
4	Team 5	86	3368	2762	1209	633	920	4631	3700	8	7	1929	1

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
Team	61	61	Team 1	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Tournament	61.0	NaN	NaN	NaN	24.0	26.827225	1.0	4.0	12.0	38.0	86.0
Score	61	61	4385	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
PlayedGames	61	53	2762	3	NaN	NaN	NaN	NaN	NaN	NaN	NaN
WonGames	61	59	7	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN
DrawnGames	61	57	14	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN
LostGames	61	56	37	3	NaN	NaN	NaN	NaN	NaN	NaN	NaN
BasketScored	61	60	70	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN
BasketGiven	61	61	3140	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
TournamentChampion	61	8	-	52	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Runner-up	61	10	-	48	NaN	NaN	NaN	NaN	NaN	NaN	NaN
TeamLaunch	61	47	1929	10	NaN	NaN	NaN	NaN	NaN	NaN	NaN
HighestPositionHeld	61.0	NaN	NaN	NaN	7.081967	5.276663	1.0	3.0	6.0	10.0	20.0

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
Team	61	61	Team 1	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Tournament	61.0	NaN	NaN	NaN	24.0	26.827225	1.0	4.0	12.0	38.0	86.0
Score	60.0	NaN	NaN	NaN	916.45	1138.342899	14.0	104.25	395.5	1360.5	4385.0
PlayedGames	60.0	NaN	NaN	NaN	810.1	877.465393	30.0	115.5	424.5	1345.5	2762.0
WonGames	60.0	NaN	NaN	NaN	309.033333	408.481395	5.0	34.75	124.0	432.75	1647.0
DrawnGames	60.0	NaN	NaN	NaN	192.083333	201.985508	4.0	26.25	98.5	331.5	633.0
LostGames	60.0	NaN	NaN	NaN	308.816667	294.508639	15.0	62.75	197.5	563.5	1070.0
BasketScored	60.0	NaN	NaN	NaN	1159.35	1512.063948	34.0	154.5	444.0	1669.75	5947.0
BasketGiven	60.0	NaN	NaN	NaN	1159.233333	1163.946914	55.0	236.0	632.5	2001.25	3889.0
TournamentChampion	9.0	NaN	NaN	NaN	9.666667	11.61895	1.0	1.0	6.0	10.0	33.0
Runner-up	13.0	NaN	NaN	NaN	6.615385	8.109033	1.0	1.0	4.0	7.0	25.0
TeamLaunch	61.0	NaN	NaN	NaN	1958.918033	27.484114	1929.0	1935.0	1951.0	1978.0	2017.0
HighestPositionHeld	61.0	NaN	NaN	NaN	7.081967	5.276663	1.0	3.0	6.0	10.0	20.0

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
0	Team 1	86.0	4385.0	2762.0	1647.0	552.0	563.0	5947.0	3140.0	33.0	23.0	1929.0	1.0
1	Team 2	86.0	4262.0	2762.0	1581.0	573.0	608.0	5900.0	3114.0	25.0	25.0	1929.0	1.0
2	Team 3	80.0	3442.0	2614.0	1241.0	598.0	775.0	4534.0	3309.0	10.0	8.0	1929.0	1.0
3	Team 4	82.0	3386.0	2664.0	1187.0	616.0	861.0	4398.0	3469.0	6.0	6.0	1931.0	1.0
4	Team 5	86.0	3368.0	2762.0	1209.0	633.0	920.0	4631.0	3700.0	8.0	7.0	1929.0	1.0

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TopTwoFinishes	TeamLaunch	HighestPositionHeld
0	Team 1	86.0	4385.0	2762.0	1647.0	552.0	563.0	5947.0	3140.0	33.0	23.0	56.0	1929.0	1.0
1	Team 2	86.0	4262.0	2762.0	1581.0	573.0	608.0	5900.0	3114.0	25.0	25.0	50.0	1929.0	1.0
2	Team 3	80.0	3442.0	2614.0	1241.0	598.0	775.0	4534.0	3309.0	10.0	8.0	18.0	1929.0	1.0
3	Team 4	82.0	3386.0	2664.0	1187.0	616.0	861.0	4398.0	3469.0	6.0	6.0	12.0	1931.0	1.0
4	Team 5	86.0	3368.0	2762.0	1209.0	633.0	920.0	4631.0	3700.0	8.0	7.0	15.0	1929.0	1.0

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
56	Team 57	1	34	38	8	10	20	38	66	-	-	2009-10	20
57	Team 58	1	22	30	7	8	15	37	57	-	-	1956-57	16
58	Team 59	1	19	30	7	5	18	51	85	-	-	1951~52	16
59	Team 60	1	14	30	5	4	21	34	65	-	-	1955-56	15
60	Team 61	1	-	-	-	-	-	-	-	-	-	2017~18	9

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
56	Team 57	1	34	38	8	10	20	38	66	-	-	2009	20
57	Team 58	1	22	30	7	8	15	37	57	-	-	1956	16
58	Team 59	1	19	30	7	5	18	51	85	-	-	1951	16
59	Team 60	1	14	30	5	4	21	34	65	-	-	1955	15
60	Team 61	1	-	-	-	-	-	-	-	-	-	2017	9

	Startup	Product	Funding	Event	Result	OperatingState
0	2600Hz	2600hz.com	NaN	Disrupt SF 2013	Contestant	Operating
1	3DLT	3dlt.com	$630K	Disrupt NYC 2013	Contestant	Closed
2	3DPrinterOS	3dprinteros.com	NaN	Disrupt SF 2016	Contestant	Operating
3	3Dprintler	3dprintler.com	$1M	Disrupt NY 2016	Audience choice	Operating
4	42 Technologies	42technologies.com	NaN	Disrupt NYC 2013	Contestant	Operating

	count	unique	top	freq
Startup	662	662	2600Hz	1
Product	656	656	2600hz.com	1
Funding	448	240	$1M	17
Event	662	26	TC50 2008	52
Result	662	5	Contestant	488
OperatingState	662	4	Operating	465

	Startup	Product	Funding	Event	Result	OperatingState	Funds_in_million
6	8 Securities	8securities.com	$29M	Disrupt Beijing 2011	Finalist	Operating	29.0
31	Anyclip	anyclip.com	$24M	TC50 2009	Finalist	Operating	24.0
40	Artsy	artsy.net	$50.9M	Disrupt NYC 2010	Contestant	Operating	50.9
49	Badgeville	badgeville.com	$40M	Disrupt SF 2010	Finalist	Acquired	40.0
56	Betterment	betterment.com	$205M	Disrupt NYC 2010	Finalist	Operating	205.0

Applied Statistics Project¶

Author: Abhinav Roy¶

Date - 18/09/2022¶

Part A¶

End of Part A¶

Part B¶

Inspect the dataset¶

Data Cleaning¶

Data Visualization¶

Univariate Analysis¶

From the above boxplots, we can observe that data points for most box plots lie to the left of the medians, therefore making the distributions Right-Skewed¶

We can see that 2 Teams have won the most number of Tournaments significantly by looking at the boxplot distribution and outliers. Likewise, 2 Teams have come out as Runner-Up most number of times.¶

Further looking at fields Score, WonGames and BasketScored, we can infer that around 5-6 Teams have performed considerably better in most Tournaments than most other Teams.¶

Multivariate Analysis¶

Doing Multivariate Analysis before Bivariate Analysis to find correlation/ relationships between different fields in the dataset¶

We can then perform Bivariate Analysis on particular fields/ attributes of interest for insights¶

Let's add some derived fields to the dataset to draw better observations from the dataset¶

Creating a Point System for the Teams would help us understand the effect of Wins/ Losses/ Draws as a whole on Team's perfomances in Tournaments¶

Point system considered --> WonGames = 3 points, LostGames = 0 points, DrawnGames = 1 point¶

The Derived Fields added are TopTwoFinishes, BasketDiff, NetGamePoints¶

From the Heatmap we can make the following observations:¶

1) TournamentChampion has a positive correlation with NetGamePoints¶

2) TournamentChampion has a strong positive correlation with BasketDiff¶

3) TournamentChampion has a very strong positive correlation with TopTwoFinishes¶

4) NetGamePoints has a very strong positive correlation with Tournament and PlayedGames¶

5) NetGamePoints has a very strong positive correlation with BasketScored and BasketGiven¶

6) NetGamePoints has a positive correlation with TopTwoFinishes¶

Bivariate Analysis¶

We can now plot a line chart to look at specfic Fields of interest¶

From the above graph we can see that only few Teams finished in the Top Two position in different tournaments. These Teams have maintained a healthy net game points and score. Team 1 and 2 have maintained a very healthy basket difference. Let's further analyse these top Teams.¶

The Teams finishing in Top 2 have a linear relationship with all the fields that determines the performance metrics for the Teams.¶

Thus, it means that as the value of the independent fields(x-axis) increases, the dependent variable(y-axis) or the number of times a Team finishes in top 2 also increases linearly.¶

The first 20 Teams have played the most number of games while maintaining higher Net Game Points throughout, which indicates that these Teams have performed well consistently throughout all competetitions.¶

Conclusions -¶

Therefore, Teams 1 and 2 can be approached by Company X for managment.¶

As an option, Teams 3, 4, 5 can also be apporached if Teams 1 and 2 are not available for management, since these Teams maintain similar healthy performance after Teams 1 and 2.¶

Suggestions -¶

Quality - The quality of data collected at source can be improved, The field TeamLaunch has mixed data format that can be avoided by only entering the year of launch.¶

Quantity - The quantity of data can be increased by collecting more information w.r.t Individual Teams/ Players/ Coaches/ Management for detailed analysis¶

Variety - The variety in data can be achieved by collecting more information about the Team attributes like, performance by decade/ location, game time for each team/ player, player ages/ throwing arm, etc. to find insights in the human factors that can affect a Teams performance¶

Velocity - The velocity of data can be maintained by ensuring that data is collected regularly after each game¶

Veracity - The veracity of data can be ensured by collecting the data from the rightful authorities/ management for analysis¶

End of Part B¶

Part C¶

Inspect the dataset¶

Data Cleaning¶

Data Visualization¶

Is there any significant difference between Funds raised by companies that are still operating vs companies that closed down ?¶

For Statistical Analysis to test for significance and conclusion, we can perform the 2 sample z-test since the sample size > 30¶

Therefore, we can conclude that the Null Hypothesis is True and that there is no significant difference between Funds raised by companies that are still operating vs companies that closed down.¶

Write your hypothesis comparing the proportion of companies that are operating between winners and contestants¶

Therefore, we can conclude that the Null Hypothesis is True and that the proportion of companies that are operating between winners and contestants is the same¶

End of Part C¶

End of Project¶